Post-design analysis for building and refining AI planning systems

• Authors: Tiago Stegun Vaquero;José Reinaldo Silva;J. Christopher Beck
• Venue: Engineering Applications of Artificial Intelligence
• Year: 2013

The growth of industrial applications of artificial intelligence has raised the need for design tools to aid in the conception and implementation of such complex systems. The design of automated planning systems faces several engineering challenges including the proper modeling of the domain knowledge: the creation of a model that represents the problem to be solved, the world that surrounds the system, and the ways the system can interact with and change the world in order to solve the problem. Knowledge modeling in AI planning is a hard task that involves acquiring the system requirements and making design decisions that can determine the behavior and performance of the resulting system. In this paper we investigate how knowledge acquired during a post-design phase of modeling can be used to improve the prospective model. A post-design framework is introduced which combines a knowledge engineering tool and a virtual prototyping environment for the analysis and simulation of plans. This framework demonstrates that post-design analysis supports the discovery of missing requirements and can guide the model refinement cycle. We present three case studies using benchmark domains and eight state-of-the-art planners. Our results demonstrate that significant improvements in plan quality and an increase in planning speed of up to three orders of magnitude can be achieved through a careful post-design process. We argue that such a process is critical for the deployment of AI planning technology in real-world engineering applications.